Make lmdb from images. Contents of lmdb. The file structure is: example.lmdb ├── data.mdb ├── lock.mdb ├── meta_info.txt The

Prepare image path list and keys for DIV2K dataset. Args: folder_path (str): Folder path. Returns: list[str]: Image path lis

Scan a directory to find the interested files. Args: dir_path (str): Path of the directory. suffix (str | tuple(str), optional):

Get the root logger. The logger will be initialized if it has not been initialized. By default a StreamHandler will be added. If `log_file` i

Numpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb.

Get bare model, especially under wrapping with DistributedDataParallel or DataParallel.

Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the co

Reorder images to 'HWC' order. If the input_order is (h, w), return (h, w, 1); If the input_order is (c, h, w), return (h, w, c); If the

Save networks and training state.

Change to Y channel of YCbCr. Args: img (ndarray): Images with range [0, 255]. Returns: (ndarray): Images with range [0, 255

Convert the type and range of the input image. It converts the input image to np.float32 type and range of [0, 1]. It is mainly used for pre-

Convert the type and range of the image according to dst_type. It converts the image to desired type and range. If `dst_type` is np.uint8, im

Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or li

Create dataloader. Args: dataset (torch.utils.data.Dataset): Dataset. dataset_opt (dict): Dataset options. It contains the follow

Create dataset. Args: dataset_opt (dict): Configuration for dataset. It constains: name (str): Dataset name. type

Create model. Args: opt (dict): Configuration. It constains: model_type (str): Model type.

Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the co

mkdirs. If path exists, rename it with timestamp and create a new one. Args: path (str): Folder path.

Validation function. Args: dataloader (torch.utils.data.DataLoader): Validation dataloader. current_iter (int): Curre

Calculate weights and indices, used for imresize function. Args: in_length (int): Input length. out_length (int): Output length.

cubic function used for calculate_weights_indices.

dict to string for printing options. Args: opt (dict): Option dict. indent_level (int): Indent level. Default: 1. Return:

Estimate AGGD (Asymmetric Generalized Gaussian Distribution) paramters. Args: block (ndarray): 2D Image block. Returns: tupl

Get environment information. Currently, only log the software version.

Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_per

Load network. Args: load_path (str): The path of networks to be loaded. net (nn.Module): Network. strict

Make dirs for experiments.

Paired random crop. It crops lists of lq and gt images with corresponding locations. Args: img_gts (list[ndarray] | ndarray): GT ima

Generate paths from lmdb. Args: folder (str): Folder path. Returns: list[str]: Returned path list.

Save networks. Args: net (nn.Module | list[nn.Module]): Network(s) to be saved. net_label (str): Network label.

Get human readable file size. Args: size (int): File size. suffix (str): Suffix. Default: 'B'. Return: str: Formated

Convert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tens

Get the initial lr, which is set by the scheduler.

Initialize slurm distributed training environment. If argument ``port`` is not specified, then the master port will be system environment var

Print keys with differnet name or different size when loading models. 1. Print keys with differnet names. 2. If strict=False, print t

Set learning rate for warmup. Args: lr_groups_l (list): List for lr_groups, each for an optimizer.

Convert a BGR image to YCbCr image. The bgr version of rgb2ycbcr. It implements the ITU-R BT.601 conversion for standard-definition telev

Check resume states and pretrain_network paths. Args: opt (dict): Options. resume_iter (int): Resume iteration.

Compute features. Args: block (ndarray): 2D Image block. Returns: list: Features with length of 18.

Performs data augmentation of the input image Input: image: a cv2 (OpenCV) image mode: int. Choice of transformation to apply

Initialize network weights. Args: module_list (list[nn.Module] | nn.Module): Modules to be initialized. scale (float): Scale init

Dequantize an array. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): M

Recover from quantized flow. Args: dx (ndarray): Quantized dx. dy (ndarray): Quantized dy. max_val (float): Maximum value

Dynamically instantiate class. Args: modules (list[importlib modules]): List of modules from importlib files. cls_typ

Generate Gaussian kernel used in `duf_downsample`. Args: kernel_size (int): Kernel size. Default: 13. sigma (float): Sigma of the

We now only use wandb to sync tensorboard log.